Enhancing quality control and safety through smart sensors and predictive maintenance models

In the last decade, rapid developments in sensing technologies have generated an unparalleled quantity of data associated with industrial processes, enabling a shift from traditional quality control practices to quality aware production chains. The availability of smart sensors for condition and quality monitoring, together with advances in predictive maintenance models, can contribute to the reduction of production variability, increased end-product quality, and optimization of safety and cost effectiveness. Noting that quality control has been traditionally associated with the inspection of finished products and processes, the opportunity for the development of quality aware production processes is studied. It is emphasized that efforts should be directed towards ensuring that the necessary conditions are satisfied while the product is under construction. If actions are undertaken to control and optimize the key process variables associated with quality, then the finished products will not need to undergo expensive destructive inspection when their quality is assessed. It is recognized that the incentive for the development of real time quality control practices lies with the reduction of production costs, achieved through reduced inspection requirements and lower warranty costs. Furthermore, real-time information associated with product quality can utilize causal databases to allow companies to optimize their quality, by directing them to situations in which the establishment of product quality is compromised. Traditionally quality control would not look for insights into production improvement, while today by employing modern quality control practices, companies can fulfill today’s expectations for product quality, reliability, and safety.